AI Agents for Coding in 2025
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This study from UC San Diego and Cornell examines how experienced software developers (3+ years) actually use AI coding agents, through field observations (N=13) and surveys (N=99). The key finding: professional developers don't "vibe code" - they carefully control agents through planning and supervision. - **Control over vibing:** Unlike the "vibe coding" trend where developers trust AI without reviewing code, experienced professionals maintain careful oversight. They plan before implementing and validate all agentic outputs to ensure software quality. - **Productivity with quality:** Developers value agents as a productivity boost while still prioritizing software quality attributes. Some reported feeling their productivity increased tenfold, though they emphasized maintaining control over the process. - **Task suitability:** Agents perform well on well-described, straightforward tasks but struggle with complex tasks. The study found agents suitable for code generation, debugging, and boilerplate but less effective for architectural decisions. - **Positive sentiment with control:** Developers generally enjoy using agents as long as they remain in control. A notable randomized trial found experienced maintainers were actually slowed by 19% when using AI, highlighting the importance of proper integration strategies.
Control over vibing: Unlike the “vibe coding” trend, where developers trust AI without reviewing code, experienced professionals maintain careful oversight. They plan before implementing and validate all agentic outputs to ensure software quality.
Productivity with quality: Developers value agents as a productivity boost while still prioritizing software quality attributes. Some reported feeling their productivity increased tenfold, though they emphasized maintaining control over the process.
Task suitability: Agents perform well on well-described, straightforward tasks but struggle with complex tasks. The study found agents suitable for code generation, debugging, and boilerplate, but less effective for architectural decisions.
Positive sentiment with control: Developers generally enjoy using agents as long as they remain in control. A notable randomized trial found experienced maintainers were actually slowed by 19% when using AI, highlighting the importance of proper integration strategies.
Abstract
The rise of AI agents is transforming how software can be built. The promise of agents is that developers might write code quicker, delegate multiple tasks to different agents, and even write a full piece of software purely out of natural language. In reality, what roles agents play in professional software development remains in question. This paper investigates how experienced developers use agents in building software, including their motivations, strategies, task suitability, and sentiments. Through field observations (N=13) and qualitative surveys (N=99), we find that while experienced developers value agents as a productivity boost, they retain their agency in software design and implementation out of insistence on fundamental software quality attributes, employing strategies for controlling agent behavior leveraging their expertise. In addition, experienced developers feel overall positive about incorporating agents into software development given their confidence in complementing the agents' limitations. Our results shed light on the value of software development best practices in effective use of agents, suggest the kinds of tasks for which agents may be suitable, and point towards future opportunities for better agentic interfaces and agentic use guidelines.
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